Abstract
PurposeThis study seeks to develop an Environmental Sustainability Balanced Scorecard (ESBSC) articulated through a strategic map for collaborative implementation by municipalities by municipalities. In addition, it aims to elucidate the architecture of this tool.Design/methodology/approachThe research uses qualitative methodology, initiating with document analysis, followed by municipal-level surveys and an interview with the Norte Portugal Regional Coordination and Development Commission (CCDR-N).FindingsThe study constructs an ESBSC that adopts an integrative approach to sustainability, focusing on municipal joint action. The tool fosters synergies and enhances cooperation. By incorporating a strategic mix, the tool contributes to improving the environmental management performance of the participating municipalities.Practical implicationsThis study introduces a tool designed for municipalities that aspire to incorporate environmental sustainability into their strategies. This tool facilitates the implementation and management of a long-term environmental strategy, with potential implications for organization and its culture. In addition, it highlights critical environmental factors that should serve as a starting point in future studies or applications of this tool.Social implicationsInvolving both an academic institution and multiple municipalities, this research identifies critical environmental factors that enhance environmental awareness within municipalities and designs a tool that, when consciously adopted, can influence the culture dynamics of the population involved. Furthermore, it proposes a structured and systematic research method for creating an ESBSC for joint municipal action.Originality/valueTo the best of authors' knowledge, this research constitutes the first exploratory attempt to devise an environmental strategy for joint municipal action. Although the tool emphasizes the environmental component, it promotes an integrated vision of sustainability. Despite the extensive application of balanced scorecards in various organizational contexts, their utilization in fostering environmental sustainability at a municipal level remains underexplored. This study addresses this gap by developing a tailored strategic tool that operationalizes environmental priorities within municipal governance frameworks.

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The domain of Human Activity Recognition (HAR) has undergone a remarkable evolution, driven by advancements in sensor technology, artificial intelligence (AI), and machine learning algorithms. The aim of this article consists of taking as a basis the previously obtained results to implement other techniques to analyze the same dataset and improve the results previously obtained in the different studies, such as neural networks with different configurations, random forest, support vector machine, CN2 rule inducer, Naive Bayes, and AdaBoost. The methodology consists of data collection from smartphone sensors, data cleaning and normalization, feature extraction techniques, and the implementation of various machine learning models. The study analyzed machine learning models for recognizing human activities using data from smartphone sensors. The results showed that the neural network and random forest models were highly effective across multiple metrics. The models achieved an area under the curve (AUC) of 98.42%, a classification accuracy of 90.14%, an F1-score of 90.13%, a precision of 90.18%, and a recall of 90.14%. With significantly reduced computational cost, our approach outperforms earlier models using the same dataset and achieves results comparable to those of contemporary deep learning-based approaches. Unlike prior studies, our work utilizes non-normalized data and integrates magnetometer signals to enhance performance, all while employing lightweight models within a reproducible visual workflow. This approach is novel, efficient, and deployable on mobile devices in real-time. This approach makes it an ideal fit for real-time mobile applications.

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Deep learning models have excelled in computer vision tasks in the past decade, but their lack of transparency raises ethical and legal concerns, especially in high-stakes areas such as surveillance and law enforcement. As such, regulations like the European Union's General Data Protection Regulation are now demanding interpretable Artificial Intelligence systems. This paper focuses on automatic face recognition, where existing systems lack interpretability and research into explainable alternatives is limited. To address this gap, we propose two interpretable facial verification models based on Siamese Networks that match and compare semantically-aligned local regions in the images. Experiments show these models rival and even outperform traditional baselines while offering clearer, more accountable explanations, advancing ethical and legally compliant facial recognition.

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Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.

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